Taille leicht schmaler, Beine minimal schmaler, Arme definierter.
ChatGPT Body Editor
Prompt ChatGPT to edit marked body areas.
Upload a photo, paint the area you want to change, and send the instruction to the server-side ChatGPT image editor. The API key stays on the backend; the frontend only sends your prompt, reference image, and mask.
Upload or use the demo photo, mark an edit area, then prompt ChatGPT.
Visualization of a possible aesthetic goal type, not a medical prediction or guaranteed transformation.
Body Goals
Preset-driven bodyshapes with bounded transformations.
Taille schmaler, Huefte und Glutes voller, Brustbereich minimal voller.
Staerkere Schultern, definierter Core und athletische Beine.
Glute-/Hueftbereich betont, Taille leicht schmaler.
Use cases
Built for low-cost iteration before premium AI rendering.
Social media and dating profiles
Preview small edits before sharing. The product emphasizes natural-looking changes instead of heavy filters.
Fitness goal visualization
Try waist, arm, leg, posture, and muscle-tone directions before turning a goal into a PerfectYou plan.
Fashion and portfolio drafts
Quickly test silhouette ideas in product, lookbook, and creator photos without scheduling a reshoot.
Why this version
A ChatGPT-style workflow with backend-held credentials.
Prompt-led editing
Plain-language prompts are sent to the server-side image model together with the uploaded photo and marking mask.
Brush marking
The editor lets users paint exactly where ChatGPT should edit instead of relying only on a free-form prompt.
Upgrade-ready
The interface can later connect to segmentation, keypoints, and generative inpainting when budget allows.
FAQ
Does this really prompt ChatGPT?
Yes. The frontend sends the prompt, source image id, preset state, and optional brush mask to the backend ChatGPT image-render endpoint.
Does the API key leave the backend?
No. The frontend never receives the API key. It only calls the PerfectYou backend endpoint.
Can this later become a premium AI editor?
Yes. The workflow is designed so a future backend can add segmentation, keypoints, and inpainting behind the same UI.